{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Survival Analysis: the Kaplan-Meier estimator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from lifelines import KaplanMeierFitter \n",
    "from lifelines.statistics import (logrank_test, \n",
    "                                  pairwise_logrank_test, \n",
    "                                  multivariate_logrank_test, \n",
    "                                  survival_difference_at_fixed_point_in_time_test)\n",
    "\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and preparing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the DataFrame: (7043, 22)\n",
      "Number of unique users in the DataFrame: 7043\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... TechSupport  \\\n",
       "0  No phone service             DSL             No  ...          No   \n",
       "1                No             DSL            Yes  ...          No   \n",
       "2                No             DSL            Yes  ...          No   \n",
       "3  No phone service             DSL            Yes  ...         Yes   \n",
       "4                No     Fiber optic             No  ...          No   \n",
       "\n",
       "  StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No              No  Month-to-month              Yes   \n",
       "1          No              No        One year               No   \n",
       "2          No              No  Month-to-month              Yes   \n",
       "3          No              No        One year               No   \n",
       "4          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges TotalCharges  Churn churn  \n",
       "0           Electronic check          29.85        29.85     No     0  \n",
       "1               Mailed check          56.95       1889.5     No     0  \n",
       "2               Mailed check          53.85       108.15    Yes     1  \n",
       "3  Bank transfer (automatic)          42.30      1840.75     No     0  \n",
       "4           Electronic check          70.70       151.65    Yes     1  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/telco_customer_churn.csv')\n",
    "df['churn'] = [1 if x == 'Yes' else 0 for x in df['Churn']]\n",
    "\n",
    "print(f\"Shape of the DataFrame: {df.shape}\")\n",
    "print(f\"Number of unique users in the DataFrame: {df['customerID'].nunique()}\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kaplan-Meier Estimate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General survival curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 406,
       "width": 566
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "T = df['tenure']\n",
    "E = df['churn']\n",
    "\n",
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(T, event_observed=E)\n",
    "\n",
    "kmf.plot(at_risk_counts=True)\n",
    "plt.title('Kaplan-Meier Curve');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "inf"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.median_survival_time_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Survival curves for each payment method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Electronic check             2365\n",
       "Mailed check                 1612\n",
       "Bank transfer (automatic)    1544\n",
       "Credit card (automatic)      1522\n",
       "Name: PaymentMethod, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PaymentMethod'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 358,
       "width": 481
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(111)\n",
    "\n",
    "kmf = KaplanMeierFitter()\n",
    "\n",
    "for payment_method in df['PaymentMethod'].unique():\n",
    "    \n",
    "    flag = df['PaymentMethod'] == payment_method\n",
    "    \n",
    "    kmf.fit(T[flag], event_observed=E[flag], label=payment_method)\n",
    "    kmf.plot(ax=ax)\n",
    "\n",
    "plt.title(\"Survival curves by payment methods\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The log-rank test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.87</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "credit_card_flag = df['PaymentMethod'] == 'Credit card (automatic)'\n",
    "bank_transfer_flag = df['PaymentMethod'] == 'Bank transfer (automatic)'\n",
    "\n",
    "results = logrank_test(T[credit_card_flag], \n",
    "                       T[bank_transfer_flag], \n",
    "                       E[credit_card_flag], \n",
    "                       E[bank_transfer_flag])\n",
    "results.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Bank transfer (automatic)</th>\n",
       "      <th>Credit card (automatic)</th>\n",
       "      <td>0.87</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic check</th>\n",
       "      <td>510.04</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mailed check</th>\n",
       "      <td>51.07</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Credit card (automatic)</th>\n",
       "      <th>Electronic check</th>\n",
       "      <td>539.74</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mailed check</th>\n",
       "      <td>64.82</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic check</th>\n",
       "      <th>Mailed check</th>\n",
       "      <td>152.46</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = pairwise_logrank_test(df['tenure'], df['PaymentMethod'], df['churn'])\n",
    "results.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>multivariate_logrank_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>865.24</td>\n",
       "      <td>&lt;0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = multivariate_logrank_test(df['tenure'], df['PaymentMethod'], df['churn'])\n",
    "results.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing survival difference at fixed point in time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>point_in_time</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>survival_difference_at_fixed_point_in_time_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = survival_difference_at_fixed_point_in_time_test(40, \n",
    "                                                          T[credit_card_flag], \n",
    "                                                          T[bank_transfer_flag], \n",
    "                                                          E[credit_card_flag], \n",
    "                                                          E[bank_transfer_flag])\n",
    "results.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>null_distribution</th>\n",
       "      <td>chi squared</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degrees_of_freedom</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>point_in_time</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_name</th>\n",
       "      <td>survival_difference_at_fixed_point_in_time_test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_statistic</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.57</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = survival_difference_at_fixed_point_in_time_test(60, \n",
    "                                                          T[credit_card_flag], \n",
    "                                                          T[bank_transfer_flag], \n",
    "                                                          E[credit_card_flag], \n",
    "                                                          E[bank_transfer_flag])\n",
    "results.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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